On the tidyverse

The tidyverse is a collection of R packages designed for data science. Developed with high standards of coding practices and software development, these packages form a consistent ecosystem to handle and visualise data. In this practical, we will mostly rely on the following packages:

• dplyr for handling data, making new variables, building summaries, number-crunching

• tidyr to re-arrange tidy/data

• ggplot2 to visualise data

• magrittr for the piping operator (%>%)

Most of these functionalities are summarised in handy cheatsheets. We provide links to the most relevant ones below:

• basics of R (not tidyverse, but they remain very useful)

• data transformation using dplyr

• data visualisation using ggplot2

Other packages

Other packages we will use include:

• rmarkdown for automated report generation

• rio to read .xlsx files in

• ape for phylogenetic reconstruction

Cheatsheets and other resources:

• the rmarkdown cheatsheet

• the knitr website documenting many options used in rmarkdown’s settings and code chunks

On the RECONverse

Several RECON packages will be used in the practical:

• linelist for data cleaning

• incidence older package to build and visualise epidemic curves, and do basic model fitting; it is being replaced by incidence2 but some older packages like earlyR (for estimating the reproduction number) and projections (for short term forecasting) are still using it.

• epicontacts to visualise and analyse contact tracing data or transmission chains

• epitrix to estimate delay distributions

• distcrete to handle delay distributions

• EpiEstim for estimation of the reproduction number over time

• outbreaker2 for outbreak reconstruction using multiple data sources

Note that earlyR and projections will soon be adapted to handle incidence2 objects.

For some of these packages, we recommend using the github (development) version, either because it is more up-to-date, or because they have not been released on CRAN yet.

## instructions for github packages (latest versions)
remotes::install_github("reconhub/linelist")

What we will do today

This practical will follow up on the response to the fictitious Ebola outbreak in Morporkia. It will build and consolidate material seen in previous sessions, and will show how genetic data can be used to reconstruct transmission trees (who infects whom?), especially when combined with epidemiological data. We will particularly look at:

• cleaning / standardising ‘dirty’ data
• building epidemic curves
• estimate serial interval distributions of contact data
• build new distributions using parameters reported in the literature
• visualise and analyse transmission chains / contact tracing data
• estimate growth rates from epidemic curves
• estimate time varying reproduction number
• read in genetic data
• make a simple phylogenetic tree
• combine epidemiological and genetic data to reconstruct transmission trees

In addition, we strongly recommend storing all analyses into one or several rmarkdown reports.

How this document works

This practical will take you through the reconstruction of a fictitious Ebola outbreak using various sources of data. It is pitched at an intermediate level, so that a fair fraction of the code is to be generated by the student. Wherever this is the case, code seen in previous practicals should come in handy. In all instances, try to first answer your own questions: try/error is a great way to learn R. But if you feel stuck, do not hesitate to questions to the demonstrators: they are here to help.

It is important that you understand what the code does. Look at relevant documentation, try tweaking the code, experiment, and do not hesitate to ask questions. It is not essential to go through all examples to illustrate important points, so take your time, especially if you are new to R. Also note that the solutions provided are by not means the only options for solving a given problem - feel free to explore alternatives and discuss them with the trainers.

The new data

The data update includes new linelists and contact lists:

• PHM-EVD-linelist-2017-12-01.xlsx: a linelist containing case information up to the 1st December 2017

• PHM-EVD-contacts-2017-12-01.xlsx: a list of contacts reported between cases up to the 1st December 2017, where from indicates a potential source of infection, and to the recipient of the contact

# load tidyverse and linelist
library(tidyverse)
library(linelist)

# read data files in
linelist_raw <- here::here("data", "phm_evd_linelist_2017-12-01.xlsx") %>%
  rio::import() %>%
  tibble()

contacts_raw <-  here::here("data", "phm_evd_contacts_2017-12-01.xlsx") %>%
  rio::import() %>%
  tibble()

cleaning_rules <- here::here("data", "phm_evd_cleaning_rules.xlsx") %>% 
  rio::import()

# clean data
linelist <- linelist_raw %>%
  clean_data(wordlist = cleaning_rules) %>% # note: guess_dates = TRUE would work here
  mutate(across(contains("date"), guess_dates))

## clean contacts
contacts <- clean_data(contacts_raw)

Once imported and cleaned, the data should look like:

# linelist: one line per case
linelist
## # A tibble: 50 × 6
##    case_id date_of_onset date_of_report sex      age location    
##    <chr>   <date>        <date>         <chr>  <dbl> <chr>       
##  1 39e9dc  2017-10-10    2017-10-22     female    62 pseudopolis 
##  2 664549  2017-10-16    2017-10-24     male      28 pseudopolis 
##  3 b4d8aa  2017-10-17    2017-10-23     male      54 ankh_morpork
##  4 51883d  2017-10-18    2017-10-22     male      57 pseudopolis 
##  5 947e40  2017-10-20    2017-10-25     female    23 ankh_morpork
##  6 9aa197  2017-10-20    2017-10-23     female    66 pseudopolis 
##  7 e4b0a2  2017-10-21    2017-10-24     female    13 ankh_morpork
##  8 af0ac0  2017-10-21    2017-10-26     male      10 ankh_morpork
##  9 185911  2017-10-21    2017-10-26     female    34 ankh_morpork
## 10 601d2e  2017-10-22    2017-10-28     male      11 ankh_morpork
## # … with 40 more rows

# contacts: pairs of cases with reported contacts
contacts
## # A tibble: 36 × 2
##    source_case_id case_id
##    <chr>          <chr>  
##  1 9aa197         426b6d 
##  2 39e9dc         a8e9d8 
##  3 c2a389         95fc1d 
##  4 51883d         778316 
##  5 51883d         e37897 
##  6 933811         99abbe 
##  7 51883d         185911 
##  8 b4d8aa         e4b0a2 
##  9 605322         1875e2 
## 10 b4d8aa         b5ad13 
## # … with 26 more rows

Analysis of contact data

After the initial stage of the outbreak, contact tracing has been maintained but started being sparser, and exposures haven’t been reported for all cases. Despite these limitations, contacts are still a valuable source of information. Using the function make_epicontacts in the epicontacts package, create a new epicontacts object called x, specifying that contacts are directed. When plotting the data, use the arguments node_shape and shapes (see ?vis_epicontacts) to distinguish males and females. Also, use node_color to distinguish individuals from Pseudopolis and Ankh Morpork. For a list of available symbols and corresponding shape code, you can type epicontacts::codeawesome.

The results should look like:

library(epicontacts)
x <- make_epicontacts(linelist, contacts, directed = TRUE)
x
## 
## /// Epidemiological Contacts //
## 
##   // class: epicontacts
##   // 50 cases in linelist; 36 contacts;  directed 
## 
##   // linelist
## 
## # A tibble: 50 × 6
##    id     date_of_onset date_of_report sex      age location    
##    <chr>  <date>        <date>         <chr>  <dbl> <chr>       
##  1 39e9dc 2017-10-10    2017-10-22     female    62 pseudopolis 
##  2 664549 2017-10-16    2017-10-24     male      28 pseudopolis 
##  3 b4d8aa 2017-10-17    2017-10-23     male      54 ankh_morpork
##  4 51883d 2017-10-18    2017-10-22     male      57 pseudopolis 
##  5 947e40 2017-10-20    2017-10-25     female    23 ankh_morpork
##  6 9aa197 2017-10-20    2017-10-23     female    66 pseudopolis 
##  7 e4b0a2 2017-10-21    2017-10-24     female    13 ankh_morpork
##  8 af0ac0 2017-10-21    2017-10-26     male      10 ankh_morpork
##  9 185911 2017-10-21    2017-10-26     female    34 ankh_morpork
## 10 601d2e 2017-10-22    2017-10-28     male      11 ankh_morpork
## # … with 40 more rows
## 
##   // contacts
## 
## # A tibble: 36 × 2
##    from   to    
##    <chr>  <chr> 
##  1 9aa197 426b6d
##  2 39e9dc a8e9d8
##  3 c2a389 95fc1d
##  4 51883d 778316
##  5 51883d e37897
##  6 933811 99abbe
##  7 51883d 185911
##  8 b4d8aa e4b0a2
##  9 605322 1875e2
## 10 b4d8aa b5ad13
## # … with 26 more rows
p <- plot(x, node_shape = "sex",
          node_color = "location",
          shapes = c(male = "male", female = "female"),
          selector = FALSE)
p

What can you say about these contacts? How would you interpret the different clusters?

x %>%
  get_pairwise("location", f = table) %>% # extract location patterns
  chisq.test(simulate = TRUE) # test, non-parametric because of low counts
## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000
##  replicates)
## 
## data:  .
## X-squared = 0.34315, df = NA, p-value = 0.6767

Explanation:

We build a contingency table using the locations of the infectors and the infectees using get_pairwise, and test whether there two variables are randomly associated using a Chi-square test. The non-parametric version of the test is used because of the low counts, which could bias the parametric version. Results show no association: the colleague from PHM is wrong in his statement that infections in Ankh Morpork mostly come from Pseudopolis.

Estimating growth rates from epidemic curves

# 'old way'

library(incidence)
i <- incidence::incidence(linelist$date_of_onset, groups = linelist$location)
plot(i)

fit(i)
## <incidence_fit object>
## 
## $model: regression of log-incidence over time
## 
## $info: list containing the following items:
##   $r (daily growth rate):
## ankh_morpork  pseudopolis 
##   0.02199783   0.01423389 
## 
##   $r.conf (confidence interval):
##                    2.5 %     97.5 %
## ankh_morpork -0.02594378 0.06993943
## pseudopolis  -0.10150740 0.12997517
## 
##   $doubling (doubling time in days):
## ankh_morpork  pseudopolis 
##     31.50980     48.69697 
## 
##   $doubling.conf (confidence interval):
##                 2.5 %     97.5 %
## ankh_morpork 9.910677 -26.717275
## pseudopolis  5.332920  -6.828539
## 
##   $pred: data.frame of incidence predictions (10 rows, 6 columns)

# new way
library(incidence2)
library(i2extras)

i <- linelist %>%
  incidence2::incidence(date_of_onset, groups = location)

i %>% 
  fit_curve(model = "poisson") %>%
  plot(angle = 45)

i %>% 
  fit_curve(model = "poisson") %>%
  growth_rate()
## # A tibble: 2 × 10
##   count_variable location    model       r r_lower r_upper growth_or_decay  time
##   <chr>          <chr>       <lis>   <dbl>   <dbl>   <dbl> <chr>           <dbl>
## 1 count          ankh_morpo… <glm> 0.00301 -0.0256  0.0324 doubling        230. 
## 2 count          pseudopolis <glm> 0.00865 -0.0344  0.0530 doubling         80.1
## # … with 2 more variables: time_lower <dbl>, time_upper <dbl>

Explanation:

Results are not very conclusive, whether the ‘old’ or the ‘new’ approaches are used. The ‘old’ approach uses a log-linear model which ignores days of zero incidence, so that results can effectively be biased. The Poisson model should be a better option, but the fit as shown in the plot is very poor. Quite possibly, this is because there has not been a single temporal trend, and transmissibility may have changed since the beginning of the outbreak. For instance, the apparent growth in Ankh Morpork disappears when considering only data from the 1st November 2017 onward. A good complementary analysis will be to estimate time-varying reproduction numbers using EpiEstim.

Estimating time-varying reproduction numbers

When the assumption that \(R\) is constant over time becomes untenable, an alternative is the estimationg of time-varying transmissibility using the instantaneous reproduction number \(R_t\). This approach, introduced by Cori et al. (Cori et al. 2013), is implemented in the package EpiEstim (function estimate_R). It estimates \(R_t\) for a succession of sliding time windows, using the same Poisson branching process model as in earlyR. In the following, we:

• estimate the serial interval by fitting a discretized Gamma distribution to the empirical data derived from the transmission chains

• build an incidence object to serve as input to estimate_R; note that new incidence2 objects will soon be possible inputs for estimate_R as well

• use estimate_R to estimate the time-varying reproduction number

library(incidence)
library(EpiEstim)

# estimate the serial interval
si <- x %>%
  get_pairwise("date_of_onset") %>% # extract empirical data from chains
  epitrix::fit_disc_gamma() # fit discretised Gamma
si
## $mu
## [1] 13.09018
## 
## $cv
## [1] 0.6489035
## 
## $sd
## [1] 8.494266
## 
## $ll
## [1] -122.525
## 
## $converged
## [1] TRUE
## 
## $distribution
## A discrete distribution
##   name: gamma
##   parameters:
##     shape: 2.37486979332248
##     scale: 5.51195897078819

Rt <- incidence::incidence(linelist$date_of_onset) %>%
  estimate_R(method = "parametric_si",
             config = make_config(mean_si = si$mu,
                                  std_si = si$sd))

plot(Rt)

Looking at Whole Genome Sequences (WGS)

WGS have been obtained for all cases in this outbreak. They are stored as a the fasta file phm_evd_wgs.fa). Download this file, save it in the data/ folder, and then run the code below to import the data in R:

library(ape)
dna_file <- here::here("data", "phm_evd_wgs.fa")
dna <- read.FASTA(dna_file)
dna
## 50 DNA sequences in binary format stored in a list.
## 
## All sequences of same length: 18958 
## 
## Labels:
## 39e9dc
## 664549
## b4d8aa
## 51883d
## 947e40
## 9aa197
## ...
## 
## Base composition:
##     a     c     g     t 
## 0.250 0.248 0.248 0.254 
## (Total: 947.9 kb)
identical(labels(dna), linelist$case_id) # check sequences match linelist data
## [1] TRUE

As a first exploration of the data, we derive a Neighbour-Joining tree rooted at the first case of the outbreak:

nj <- nj(dist.dna(dna, model = "N")) # NJ on nucleotide distances (model = "N")
nj
## 
## Phylogenetic tree with 50 tips and 48 internal nodes.
## 
## Tip labels:
##   39e9dc, 664549, b4d8aa, 51883d, 947e40, 9aa197, ...
## 
## Unrooted; includes branch lengths.
nj <- root(nj, 1)
plot(nj, main = "Neighbour Joining tree")
axisPhylo()

This phylogenetic tree shows the inferred evolution of the pathogen sequences. Branch length (x-axis) correspond to the number of mutations occuring between lineages (indicated by the axis at the bottom). The tree has been rooted to the index case, so that this sequence (top, left) is the “most ancient” part of the tree. Note that in such representations, distances on the y-axis are meaningless.

Building delay distributions

outbreaker2 can handle different types of dates. When dates of onset are provided, information on the generation time (delay between primary and secondary infections) and on the incubation period (delay between infection and symptom onset) can be included in the model. These delays are typically modelled as Gamma distributions, which need to be discretised in order to account for the fact that time is reported as days.

Here, we will use the serial interval estimated from the transmission pairs as a proxy for the generation time. For the incubation period, your colleagues from PHM report that they observed average incubation times of 10.6 days with a standard deviation of 7.4 days. To build a discretized Gamma distribution from this data, we will:

• use epitrix’s function gamma_mucv2shapescale to convert these parameters into shape and scale for a Gamma distribution

• then use distcrete to generate discretised distributions using shape and scale

library(epitrix)

# find parameters for Gamma
incub_mu <- 10.6
incub_sd <- 7.4
incub_cv <- incub_sd / incub_mu

incub_params <- gamma_mucv2shapescale(incub_mu, incub_cv)

# build discretized gamma
incub <- distcrete::distcrete("gamma",
                              shape = incub_params$shape,
                              scale = incub_params$scale,
                              w = 0.5, interval = 1)
incub
## A discrete distribution
##   name: gamma
##   parameters:
##     shape: 2.05186267348429
##     scale: 5.16603773584906

# check resulting distribution
tibble(days = 0:30, p = incub$d(0:30)) %>%
  ggplot(aes(x = days, y = p)) +
  geom_col() +
  theme_bw() +
  labs(title = "Incubation period distribution",
       x = "Days from infection to onset of symptoms",
       y = "Probability")

Similarly, we can visualise the serial interval distribution previously estimated from the transmission chains:

tibble(days = 0:50, p = si$distribution$d(0:50)) %>%
  ggplot(aes(x = days, y = p)) +
  geom_col() +
  theme_bw() +
  labs(title = "Serial interval distribution",
       x = "Days between onsets of primary and secondary cases",
       y = "Probability")

Using the original outbreaker model

The original outbreaker model combined temporal information (here, dates of onset) with sequence data to infer who infected whom. Here, we use outbreaker2 to apply this model to the data.

All inputs to the new outbreaker function are prepared using dedicated functions, which make a number of checks on provided inputs and define defaults:

library(outbreaker2)

data <- outbreaker_data(
  dates = linelist$date_of_onset, # dates of onset
  dna = unname(dna), # WGS; remove labels for compatibility
  w_dens =si$distribution$d(1:100), # generation time distribution
  f_dens = incub$d(1:100) # incubation period distribution
)

We also create a configuration, which determines different aspects of the analysis, including which parameters need to be estimated, initial values of parameters, the length of the MCMC, etc.:

config <- create_config(
  move_kappa = FALSE, # don't look for missing cases
  move_pi = FALSE, # don't estimate reporting
  init_pi = 1, # set reporting to 1
  find_import = FALSE, # don't look for additional imported cases
  init_tree = "star" # star-like tree as starting point
)

We can now run the analysis. This should take a couple of minutes on modern laptops. Note the use of set.seed(0) to have identical results across different users and computers:

set.seed(0)
res_basic <- outbreaker(data = data, config = config)
res_basic
## 
## 
##  ///// outbreaker results ///
## 
## class:  outbreaker_chains data.frame
## dimensions 201 rows,  158 columns
## ancestries not shown: alpha_1 - alpha_50
## infection dates not shown: t_inf_1 - t_inf_50
## intermediate generations not shown: kappa_1 - kappa_50
## 
## /// head //
##   step       post       like    prior           mu pi eps lambda
## 1    1 -1959.8477 -1962.1502 2.302485 1.000000e-04  1 0.5   0.05
## 2   50  -814.8591  -817.1616 2.302530 5.512792e-05  1 0.5   0.05
## 3  100  -818.4481  -820.7506 2.302543 4.201736e-05  1 0.5   0.05
## 
## ...
## /// tail //
##      step      post      like    prior           mu pi eps lambda
## 199  9900 -806.4059 -808.7085 2.302530 5.487824e-05  1 0.5   0.05
## 200  9950 -805.8182 -808.1208 2.302544 4.148942e-05  1 0.5   0.05
## 201 10000 -807.9692 -810.2718 2.302535 4.962910e-05  1 0.5   0.05
plot(res_basic)

plot(res_basic, burn = 500)

plot(res_basic,  type = "alpha", burnin = 500) # ancestries

plot(res_basic,  type = "t_inf", burnin = 500) # infection dates

plot(res_basic, "mu", burn = 500, type = "density") # mutation rate

plot(res_basic,  type = "network", burnin = 500, min_support = .05) # transmission trees

As a further help for interpretation, you can derive a consensus tree from the posterior samples of trees using summary. Look in particular at the support column, and compare the results to the contact data.

smry_basic <- summary(res_basic)
head(smry_basic$tree)
##   from to time   support generations
## 1   NA  1  -10 0.2089552          NA
## 2    1  2   -1 0.4577114           1
## 3    1  3    1 0.3830846           1
## 4    1  4   -2 0.3532338           1
## 5    1  5   -4 0.5422886           1
## 6    4  6    4 0.4726368           1
tail(smry_basic$tree)
##    from to time   support generations
## 45   22 45   28 0.1691542           1
## 46   30 46   32 0.4278607           1
## 47   34 47   31 0.8358209           1
## 48   40 48   29 0.1890547           1
## 49   32 49   36 0.3333333           1
## 50   40 50   34 0.1741294           1
hist(smry_basic$tree$support, col = "grey", border = "white",
     main = "Consensus ancestry: support", xlim = c(0,1))

set.seed(0)

data <- outbreaker_data(
  dates = linelist$date_of_onset, # dates of onset
  w_dens =si$distribution$d(1:100), # generation time distribution
  f_dens = incub$d(1:100) # incubation period distribution
)
res_time <- outbreaker(data = data, config = config)
plot(res_time, type = "alpha", burn = 500)

Adding contact data to the reconstruction process

outbreaker2 also implements a module for taking contact data into account when reconstructing transmission chains (Campbell et al. 2019). Contact data currently contains case labels. While epicontacts objects will soon be accepted as inputs in outbreaker2, for now we need to operate some minor transformations to define contacts using cases indices rather than labels:

ctd <- matrix(match(unlist(x$contacts), linelist$case_id), ncol = 2)
head(ctd)
##      [,1] [,2]
## [1,]    6   28
## [2,]    1   15
## [3,]   30   46
## [4,]    4   23
## [5,]    4   13
## [6,]   41   43
dim(ctd)
## [1] 36  2

All inputs to the outbreaker function are prepared using dedicated functions, which make a number of checks on provided inputs and define defaults:

data <- outbreaker_data(
  dates = linelist$date_of_onset, # dates of onset
  dna = unname(dna), # dna sequences
  ctd = ctd, # contact data
  w_dens =si$distribution$d(1:100), # generation time distribution
  f_dens = incub$d(1:100) # incubation period distribution
)

We are now ready to run the analysis. This may take a couple of minutes, depending on your computer:

set.seed(0)
res_full <- outbreaker(data = data, config = config)
res_full
## 
## 
##  ///// outbreaker results ///
## 
## class:  outbreaker_chains data.frame
## dimensions 201 rows,  158 columns
## ancestries not shown: alpha_1 - alpha_50
## infection dates not shown: t_inf_1 - t_inf_50
## intermediate generations not shown: kappa_1 - kappa_50
## 
## /// head //
##   step       post       like    prior           mu pi       eps      lambda
## 1    1 -2133.4925 -2135.7950 2.302485 1.000000e-04  1 0.5000000 0.050000000
## 2   50  -879.8242  -882.1267 2.302529 5.583594e-05  1 0.5830505 0.009765158
## 3  100  -842.5666  -844.8691 2.302535 5.002914e-05  1 0.8447734 0.002261452
## 
## ...
## /// tail //
##      step      post      like    prior           mu pi       eps      lambda
## 199  9900 -840.6293 -842.9318 2.302527 5.781234e-05  1 0.7711070 0.001722643
## 200  9950 -836.3669 -838.6694 2.302537 4.855279e-05  1 0.6657835 0.001082511
## 201 10000 -842.4907 -844.7932 2.302524 6.097349e-05  1 0.5960041 0.002275266

Produce graphics as in the previous model. Assess convergence, choose an appropriate burnin, visualise ancestries and the infection timelines:

plot(res_full) # log-posterior trace

plot(res_full, burnin = 200) # log-posterior trace

plot(res_full,  type = "alpha", burnin = 200) # ancestries

plot(res_full,  type = "t_inf", burnin = 200) # infection dates

plot(res_full,  type = "network", burnin = 200, min_support = .05) # transmission trees

smry_full <- summary(res_full)
head(smry_full$tree)
##   from to time   support generations
## 1   NA  1   -8        NA          NA
## 2    1  2    1 0.6517413           1
## 3    1  3    1 1.0000000           1
## 4    1  4   -1 0.6616915           1
## 5    1  5   -1 1.0000000           1
## 6    4  6    5 0.6716418           1

smry_full$tree$support <- round(smry_full$tree$support, 2)

linelist$id <- 1:nrow(linelist) # add case index to linelist
cons_tree <- make_epicontacts(
  linelist,
  smry_full$tree[-1, ],
  id = "id",
  from = 1,
  to = 2,
  directed = TRUE)

In the following, we add age class information to the linelist of cons_tree, and create color palettes which will be used to display information on the final graph:

support_pal <- colorRampPalette(
    c("#918D98", "#645877", "#423359", "#281449", "#1A0340")
)

age_pal <- colorRampPalette(
    c("#3288BD", "#ABDDA4", "#FDAE61", "#D53E4F")
)

cons_tree$linelist$age_class <- cut(
  cons_tree$linelist$age,
  breaks = c(0, 10, 20, 30, 40, 90),
  labels = c("0-10", "11-20", "21-30", "31-40", "41+" ))

Looking carefully at the documentation of vis_epicontacts, try to reproduce the final consensus tree below:

plot(cons_tree, edge_col = "support", edge_col_pal = support_pal,
     edge_label = "support", node_shape = "sex",
     shapes = c(male = "male", female = "female"), node_color = "age_class",
     col_pal = age_pal)

Explanation:

When adding contact data, the transmission tree becomes much clearer, with most ancestries being well-resolved and pointing to a single, highly probable infector. We can then visualise a consensus tree (the $tree component of the list output when calling summary() on outbreaker2 objects), i.e. the tree in which only the most likely infector is kept for each case. When we add information about the age groups to the consensus tree (using colors for the nodes), it seems all ‘super-spreaders’ were from the older age group. Further investigations should be made to understand this trend. Meanwhile, it may be a good idea to prioritise contact tracing followup from the older patients. In particular, case 48, a recent case who has at least infected one person, and may have caused more secondary transmissions.